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<h1 id="training-deep-learning-probabilistic-models">Training Deep Learning Probabilistic Models<a class="headerlink" href="#training-deep-learning-probabilistic-models" title="Permanent link">&para;</a></h1>


<div class="doc doc-object doc-class">



<h2 id="pytorch_widedeep.training.BayesianTrainer" class="doc doc-heading">
            <span class="doc doc-object-name doc-class-name">BayesianTrainer</span>


<a href="#pytorch_widedeep.training.BayesianTrainer" class="headerlink" title="Permanent link">&para;</a></h2>


    <div class="doc doc-contents first">
            <p class="doc doc-class-bases">
              Bases: <code><span title="pytorch_widedeep.training._base_bayesian_trainer.BaseBayesianTrainer">BaseBayesianTrainer</span></code></p>


        <p>Class to set the of attributes that will be used during the
training process.</p>
<p>Both the Bayesian models and the Trainer in this repo are based on the paper:
<a href="https://arxiv.org/pdf/1505.05424.pdf">Weight Uncertainty in Neural Networks</a>.</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>model</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.bayesian_models._base_bayesian_model.BaseBayesianModel">BaseBayesianModel</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>An object of class <code>BaseBayesianModel</code>. See the <code>Model Components</code>
section here in the docs.</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>objective</code>
            </td>
            <td>
                  <code>str</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Defines the objective, loss or cost function.<br/>
Param aliases: <code>loss_function</code>, <code>loss_fn</code>, <code>loss</code>,
<code>cost_function</code>, <code>cost_fn</code>, <code>cost</code><br/>
Possible values are: <em>'binary'</em>, <em>'multiclass'</em>, <em>'regression'</em></p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>custom_loss_function</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.Module">Module</span>]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>If none of the loss functions available suits the user, it is possible
to pass a custom loss function. See for example
<code>pytorch_widedeep.losses.FocalLoss</code> for the required structure of the
object or the Examples folder in the repo.</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>optimizer</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.Optimizer">Optimizer</span>]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>An instance of Pytorch's <code>Optimizer</code> object(e.g. <code>torch.optim.Adam
()</code>). if no optimizer is passed it will default to <code>AdamW</code>.</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>lr_scheduler</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.LRScheduler">LRScheduler</span>]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>An instance of Pytorch's <code>LRScheduler</code> object
(e.g <code>torch.optim.lr_scheduler.StepLR(opt, step_size=5)</code>).</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>callbacks</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.List">List</span>[<span title="pytorch_widedeep.callbacks.Callback">Callback</span>]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>List with <code>Callback</code> objects. The three callbacks available in
<code>pytorch-widedeep</code> are: <code>LRHistory</code>, <code>ModelCheckpoint</code> and
<code>EarlyStopping</code>. This can also be a custom callback. See
<code>pytorch_widedeep.callbacks.Callback</code> or the Examples folder in the
repo.</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>metrics</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.Union">Union</span>[<span title="pytorch_widedeep.wdtypes.List">List</span>[<span title="pytorch_widedeep.metrics.Metric">Metric</span>], <span title="pytorch_widedeep.wdtypes.List">List</span>[<span title="torchmetrics.Metric">Metric</span>]]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <ul>
<li>List of objects of type <code>Metric</code>. Metrics available are:
  <code>Accuracy</code>, <code>Precision</code>, <code>Recall</code>, <code>FBetaScore</code>,
  <code>F1Score</code> and <code>R2Score</code>. This can also be a custom metric as
  long as it is an object of type <code>Metric</code>. See
  <code>pytorch_widedeep.metrics.Metric</code> or the Examples folder in the repo</li>
<li>List of objects of type <code>torchmetrics.Metric</code>. This can be any
  metric from torchmetrics library <a href="https://lightning.ai/docs/torchmetrics">Examples</a>
  classification-metrics&gt;<code>_. It can also be a torchmetric custom metric as
  long as it is an object of type</code>Metric<code>.
  See</code>the <a href="(https://lightning.ai/docs/torchmetrics)">instructions</a></li>
</ul>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>verbose</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Setting it to 0 will print nothing during training.</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>seed</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Random seed to be used internally for train_test_split</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Other Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td><code>**kwargs</code></td>
            <td>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Other infrequently used arguments that can also be passed as kwargs are:</p>
<ul>
<li>
<p><strong>device</strong>: <code>str</code><br/>
    string indicating the device. One of <em>'cpu'</em>, <em>'gpu'</em> or 'mps' if
    run on a Mac with Apple silicon or AMD GPU(s)</p>
</li>
<li>
<p><strong>num_workers</strong>: <code>int</code><br/>
    number of workers to be used internally by the data loaders</p>
</li>
<li>
<p><strong>class_weight</strong>: <code>List[float]</code><br/>
    This is the <code>weight</code> or <code>pos_weight</code> parameter in
    <code>CrossEntropyLoss</code> and <code>BCEWithLogitsLoss</code>, depending on whether</p>
</li>
<li>
<p><strong>reducelronplateau_criterion</strong>: <code>str</code>
    This sets the criterion that will be used by the lr scheduler to
    take a step: One of <em>'loss'</em> or <em>'metric'</em>. The ReduceLROnPlateau
    learning rate is a bit particular.</p>
</li>
</ul>
              </div>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Attributes:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.training.BayesianTrainer.cyclic_lr">cyclic_lr</span></code></td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Attribute that indicates if  the lr_scheduler is cyclic_lr
(i.e. <code>CyclicLR</code> or <code>OneCycleLR</code>). See <code>Pytorch schedulers
&lt;https://pytorch.org/docs/stable/optim.html&gt;</code>_.</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>






              <details class="quote">
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<span class="normal">508</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">BayesianTrainer</span><span class="p">(</span><span class="n">BaseBayesianTrainer</span><span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Class to set the of attributes that will be used during the</span>
<span class="sd">    training process.</span>

<span class="sd">    Both the Bayesian models and the Trainer in this repo are based on the paper:</span>
<span class="sd">    [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf).</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    model: `BaseBayesianModel`</span>
<span class="sd">        An object of class `BaseBayesianModel`. See the `Model Components`</span>
<span class="sd">        section here in the docs.</span>
<span class="sd">    objective: str</span>
<span class="sd">        Defines the objective, loss or cost function.&lt;br/&gt;</span>
<span class="sd">        Param aliases: `loss_function`, `loss_fn`, `loss`,</span>
<span class="sd">        `cost_function`, `cost_fn`, `cost`&lt;br/&gt;</span>
<span class="sd">        Possible values are: _&#39;binary&#39;_, _&#39;multiclass&#39;_, _&#39;regression&#39;_</span>
<span class="sd">    custom_loss_function: `nn.Module`, optional, default = None</span>
<span class="sd">        If none of the loss functions available suits the user, it is possible</span>
<span class="sd">        to pass a custom loss function. See for example</span>
<span class="sd">        `pytorch_widedeep.losses.FocalLoss` for the required structure of the</span>
<span class="sd">        object or the Examples folder in the repo.</span>
<span class="sd">    optimizer: `Optimzer`, optional, default= None</span>
<span class="sd">        An instance of Pytorch&#39;s `Optimizer` object(e.g. `torch.optim.Adam</span>
<span class="sd">        ()`). if no optimizer is passed it will default to `AdamW`.</span>
<span class="sd">    lr_scheduler: `LRScheduler`, optional, default=None</span>
<span class="sd">        An instance of Pytorch&#39;s `LRScheduler` object</span>
<span class="sd">        (e.g `torch.optim.lr_scheduler.StepLR(opt, step_size=5)`).</span>
<span class="sd">    callbacks: List, optional, default=None</span>
<span class="sd">        List with `Callback` objects. The three callbacks available in</span>
<span class="sd">        `pytorch-widedeep` are: `LRHistory`, `ModelCheckpoint` and</span>
<span class="sd">        `EarlyStopping`. This can also be a custom callback. See</span>
<span class="sd">        `pytorch_widedeep.callbacks.Callback` or the Examples folder in the</span>
<span class="sd">        repo.</span>
<span class="sd">    metrics: List, optional, default=None</span>
<span class="sd">        - List of objects of type `Metric`. Metrics available are:</span>
<span class="sd">          `Accuracy`, `Precision`, `Recall`, `FBetaScore`,</span>
<span class="sd">          `F1Score` and `R2Score`. This can also be a custom metric as</span>
<span class="sd">          long as it is an object of type `Metric`. See</span>
<span class="sd">          `pytorch_widedeep.metrics.Metric` or the Examples folder in the repo</span>
<span class="sd">        - List of objects of type `torchmetrics.Metric`. This can be any</span>
<span class="sd">          metric from torchmetrics library [Examples](https://lightning.ai/docs/torchmetrics)</span>
<span class="sd">          classification-metrics&gt;`_. It can also be a torchmetric custom metric as</span>
<span class="sd">          long as it is an object of type `Metric`.</span>
<span class="sd">          See `the [instructions]((https://lightning.ai/docs/torchmetrics))</span>
<span class="sd">    verbose: int, default=1</span>
<span class="sd">        Setting it to 0 will print nothing during training.</span>
<span class="sd">    seed: int, default=1</span>
<span class="sd">        Random seed to be used internally for train_test_split</span>

<span class="sd">    Other Parameters</span>
<span class="sd">    ----------------</span>
<span class="sd">    **kwargs: dict</span>
<span class="sd">        Other infrequently used arguments that can also be passed as kwargs are:</span>

<span class="sd">        - **device**: `str`&lt;br/&gt;</span>
<span class="sd">            string indicating the device. One of _&#39;cpu&#39;_, _&#39;gpu&#39;_ or &#39;mps&#39; if</span>
<span class="sd">            run on a Mac with Apple silicon or AMD GPU(s)</span>

<span class="sd">        - **num_workers**: `int`&lt;br/&gt;</span>
<span class="sd">            number of workers to be used internally by the data loaders</span>

<span class="sd">        - **class_weight**: `List[float]`&lt;br/&gt;</span>
<span class="sd">            This is the `weight` or `pos_weight` parameter in</span>
<span class="sd">            `CrossEntropyLoss` and `BCEWithLogitsLoss`, depending on whether</span>

<span class="sd">        - **reducelronplateau_criterion**: `str`</span>
<span class="sd">            This sets the criterion that will be used by the lr scheduler to</span>
<span class="sd">            take a step: One of _&#39;loss&#39;_ or _&#39;metric&#39;_. The ReduceLROnPlateau</span>
<span class="sd">            learning rate is a bit particular.</span>

<span class="sd">    Attributes</span>
<span class="sd">    ----------</span>
<span class="sd">    cyclic_lr: bool</span>
<span class="sd">        Attribute that indicates if  the lr_scheduler is cyclic_lr</span>
<span class="sd">        (i.e. `CyclicLR` or `OneCycleLR`). See `Pytorch schedulers</span>
<span class="sd">        &lt;https://pytorch.org/docs/stable/optim.html&gt;`_.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="nd">@alias</span><span class="p">(</span>  <span class="c1"># noqa: C901</span>
        <span class="s2">&quot;objective&quot;</span><span class="p">,</span>
        <span class="p">[</span><span class="s2">&quot;loss_function&quot;</span><span class="p">,</span> <span class="s2">&quot;loss_fn&quot;</span><span class="p">,</span> <span class="s2">&quot;loss&quot;</span><span class="p">,</span> <span class="s2">&quot;cost_function&quot;</span><span class="p">,</span> <span class="s2">&quot;cost_fn&quot;</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">],</span>
    <span class="p">)</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">model</span><span class="p">:</span> <span class="n">BaseBayesianModel</span><span class="p">,</span>
        <span class="n">objective</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
        <span class="n">custom_loss_function</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Module</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">optimizer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Optimizer</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">lr_scheduler</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">LRScheduler</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">callbacks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Callback</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">metrics</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Metric</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">TorchMetric</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">verbose</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
            <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
            <span class="n">objective</span><span class="o">=</span><span class="n">objective</span><span class="p">,</span>
            <span class="n">custom_loss_function</span><span class="o">=</span><span class="n">custom_loss_function</span><span class="p">,</span>
            <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
            <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler</span><span class="p">,</span>
            <span class="n">callbacks</span><span class="o">=</span><span class="n">callbacks</span><span class="p">,</span>
            <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">,</span>
            <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span>
            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>  <span class="c1"># noqa: C901</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
        <span class="n">target</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
        <span class="n">X_tab_val</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">target_val</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">val_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">n_epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">validation_freq</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
        <span class="n">n_train_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
        <span class="n">n_val_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
    <span class="p">):</span>
<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fit method.</span>

<span class="sd">        Parameters</span>
<span class="sd">        ----------</span>
<span class="sd">        X_tab: np.ndarray,</span>
<span class="sd">            tabular dataset</span>
<span class="sd">        target: np.ndarray</span>
<span class="sd">            target values</span>
<span class="sd">        X_tab_val: np.ndarray, Optional, default = None</span>
<span class="sd">            validation data</span>
<span class="sd">        target_val: np.ndarray, Optional, default = None</span>
<span class="sd">            validation target values</span>
<span class="sd">        val_split: float, Optional. default=None</span>
<span class="sd">            An alterative to passing the validation set is to use a train/val</span>
<span class="sd">            split fraction via `val_split`</span>
<span class="sd">        n_epochs: int, default=1</span>
<span class="sd">            number of epochs</span>
<span class="sd">        validation_freq: int, default=1</span>
<span class="sd">            epochs validation frequency</span>
<span class="sd">        batch_size: int, default=32</span>
<span class="sd">            batch size</span>
<span class="sd">        n_train_samples: int, default=2</span>
<span class="sd">            number of samples to average over during the training process.</span>
<span class="sd">            See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.</span>
<span class="sd">        n_val_samples: int, default=2</span>
<span class="sd">            number of samples to average over during the validation process.</span>
<span class="sd">            See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>

        <span class="n">train_set</span><span class="p">,</span> <span class="n">eval_set</span> <span class="o">=</span> <span class="n">tabular_train_val_split</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">,</span> <span class="n">X_tab</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">X_tab_val</span><span class="p">,</span> <span class="n">target_val</span><span class="p">,</span> <span class="n">val_split</span>
        <span class="p">)</span>
        <span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">dataset</span><span class="o">=</span><span class="n">train_set</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_workers</span>
        <span class="p">)</span>
        <span class="n">train_steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">eval_set</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">eval_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
                <span class="n">dataset</span><span class="o">=</span><span class="n">eval_set</span><span class="p">,</span>
                <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
                <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_workers</span><span class="p">,</span>
                <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">eval_steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_loader</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_begin</span><span class="p">(</span>
            <span class="p">{</span>
                <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="n">batch_size</span><span class="p">,</span>
                <span class="s2">&quot;train_steps&quot;</span><span class="p">:</span> <span class="n">train_steps</span><span class="p">,</span>
                <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span>
            <span class="p">}</span>
        <span class="p">)</span>
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
            <span class="n">epoch_logs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_epoch_begin</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">logs</span><span class="o">=</span><span class="n">epoch_logs</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">train_running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
            <span class="k">with</span> <span class="n">trange</span><span class="p">(</span><span class="n">train_steps</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="k">as</span> <span class="n">t</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">):</span>
                    <span class="n">t</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="s2">&quot;epoch </span><span class="si">%i</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
                    <span class="n">train_score</span><span class="p">,</span> <span class="n">train_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_step</span><span class="p">(</span>
                        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_train_samples</span><span class="p">,</span> <span class="n">train_steps</span><span class="p">,</span> <span class="n">batch_idx</span>
                    <span class="p">)</span>
                    <span class="n">print_loss_and_metric</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">train_score</span><span class="p">)</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_batch_end</span><span class="p">(</span><span class="n">batch</span><span class="o">=</span><span class="n">batch_idx</span><span class="p">)</span>
            <span class="n">epoch_logs</span> <span class="o">=</span> <span class="n">save_epoch_logs</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">train_score</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">)</span>

            <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="k">if</span> <span class="n">eval_set</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">epoch</span> <span class="o">%</span> <span class="n">validation_freq</span> <span class="o">==</span> <span class="p">(</span>
                <span class="n">validation_freq</span> <span class="o">-</span> <span class="mi">1</span>
            <span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_eval_begin</span><span class="p">()</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">valid_running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
                <span class="k">with</span> <span class="n">trange</span><span class="p">(</span><span class="n">eval_steps</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="k">as</span> <span class="n">v</span><span class="p">:</span>
                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">eval_loader</span><span class="p">):</span>
                        <span class="n">v</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="s2">&quot;valid&quot;</span><span class="p">)</span>
                        <span class="n">val_score</span><span class="p">,</span> <span class="n">val_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_step</span><span class="p">(</span>
                            <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_val_samples</span><span class="p">,</span> <span class="n">train_steps</span><span class="p">,</span> <span class="n">i</span>
                        <span class="p">)</span>
                        <span class="n">print_loss_and_metric</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_score</span><span class="p">)</span>
                <span class="n">epoch_logs</span> <span class="o">=</span> <span class="n">save_epoch_logs</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">,</span> <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_score</span><span class="p">,</span> <span class="s2">&quot;val&quot;</span><span class="p">)</span>

                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau</span><span class="p">:</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau_criterion</span> <span class="o">==</span> <span class="s2">&quot;loss&quot;</span><span class="p">:</span>
                        <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="n">val_loss</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="n">val_score</span><span class="p">[</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau_criterion</span>
                        <span class="p">]</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_epoch_end</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">epoch_logs</span><span class="p">,</span> <span class="n">on_epoch_end_metric</span><span class="p">)</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">)</span>
                <span class="k">break</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_restore_best_weights</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>  <span class="c1"># type: ignore[return]</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
        <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
        <span class="n">return_samples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the predictions</span>

<span class="sd">        Parameters</span>
<span class="sd">        ----------</span>
<span class="sd">        X_tab: np.ndarray,</span>
<span class="sd">            tabular dataset</span>
<span class="sd">        n_samples: int, default=5</span>
<span class="sd">            number of samples that will be either returned or averaged to</span>
<span class="sd">            produce an overal prediction</span>
<span class="sd">        return_samples: bool, default = False</span>
<span class="sd">            Boolean indicating whether the n samples will be averaged or directly returned</span>
<span class="sd">        batch_size: int, default = 256</span>
<span class="sd">            batch size</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        np.ndarray:</span>
<span class="sd">            array with the predictions</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="n">preds_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">return_samples</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
        <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span>
        <span class="n">axis</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="mi">1</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;regression&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span>  <span class="c1"># type: ignore[return]</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
        <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
        <span class="n">return_samples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the predicted probabilities</span>

<span class="sd">        Parameters</span>
<span class="sd">        ----------</span>
<span class="sd">        X_tab: np.ndarray,</span>
<span class="sd">            tabular dataset</span>
<span class="sd">        n_samples: int, default=5</span>
<span class="sd">            number of samples that will be either returned or averaged to</span>
<span class="sd">            produce an overal prediction</span>
<span class="sd">        return_samples: bool, default = False</span>
<span class="sd">            Boolean indicating whether the n samples will be averaged or directly returned</span>
<span class="sd">        batch_size: int, default = 256</span>
<span class="sd">            batch size</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        np.ndarray</span>
<span class="sd">            array with the probabilities per class</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">preds_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">return_samples</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
        <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">return_samples</span><span class="p">:</span>
                <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
                <span class="n">probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">])</span>
                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_samples</span><span class="p">):</span>
                    <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
                    <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
                <span class="n">probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">])</span>
                <span class="n">probs</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">preds</span>
                <span class="n">probs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">preds</span>
            <span class="k">return</span> <span class="n">probs</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">preds</span>

    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
        <span class="n">save_state_dict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">model_filename</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bayesian_model.pt&quot;</span><span class="p">,</span>
    <span class="p">):</span>
<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Saves the model, training and evaluation history to disk</span>

<span class="sd">        The `Trainer` class is built so that it &#39;just&#39; trains a model. With</span>
<span class="sd">        that in mind, all the torch related parameters (such as optimizers or</span>
<span class="sd">        learning rate schedulers) have to be defined externally and then</span>
<span class="sd">        passed to the `Trainer`. As a result, the `Trainer` does not</span>
<span class="sd">        generate any attribute or additional data products that need to be</span>
<span class="sd">        saved other than the `model` object itself, which can be saved as</span>
<span class="sd">        any other torch model (e.g. `torch.save(model, path)`).</span>

<span class="sd">        Parameters</span>
<span class="sd">        ----------</span>
<span class="sd">        path: str</span>
<span class="sd">            path to the directory where the model and the feature importance</span>
<span class="sd">            attribute will be saved.</span>
<span class="sd">        save_state_dict: bool, default = False</span>
<span class="sd">            Boolean indicating whether to save directly the model or the</span>
<span class="sd">            model&#39;s state dictionary</span>
<span class="sd">        model_filename: str, Optional, default = &quot;wd_model.pt&quot;</span>
<span class="sd">            filename where the model weights will be store</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="n">save_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
        <span class="n">history_dir</span> <span class="o">=</span> <span class="n">save_dir</span> <span class="o">/</span> <span class="s2">&quot;history&quot;</span>
        <span class="n">history_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

        <span class="c1"># the trainer is run with the History Callback by default</span>
        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">history_dir</span> <span class="o">/</span> <span class="s2">&quot;train_eval_history.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">teh</span><span class="p">:</span>
            <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">teh</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span>

        <span class="n">has_lr_history</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span>
            <span class="p">[</span><span class="n">clbk</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;LRHistory&quot;</span> <span class="k">for</span> <span class="n">clbk</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">callbacks</span><span class="p">]</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_lr_history</span><span class="p">:</span>
            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">history_dir</span> <span class="o">/</span> <span class="s2">&quot;lr_history.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">lrh</span><span class="p">:</span>
                <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_history</span><span class="p">,</span> <span class="n">lrh</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span>

        <span class="n">model_path</span> <span class="o">=</span> <span class="n">save_dir</span> <span class="o">/</span> <span class="n">model_filename</span>
        <span class="k">if</span> <span class="n">save_state_dict</span><span class="p">:</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">model_path</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">model_path</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_train_step</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
        <span class="n">target</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
        <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">n_batches</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">batch_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

        <span class="n">X</span> <span class="o">=</span> <span class="n">to_device</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">!=</span> <span class="s2">&quot;multiclass&quot;</span> <span class="k">else</span> <span class="n">target</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">to_device</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
        <span class="n">y_pred</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">sample_elbo</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_batches</span><span class="p">)</span>  <span class="c1"># type: ignore[arg-type]</span>

        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_score</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">train_running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">avg_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_running_loss</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">score</span><span class="p">,</span> <span class="n">avg_loss</span>

    <span class="k">def</span> <span class="nf">_eval_step</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
        <span class="n">target</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
        <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">n_batches</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">batch_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="n">X</span> <span class="o">=</span> <span class="n">to_device</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
            <span class="n">y</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">!=</span> <span class="s2">&quot;multiclass&quot;</span> <span class="k">else</span> <span class="n">target</span>
            <span class="n">y</span> <span class="o">=</span> <span class="n">to_device</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>

            <span class="n">y_pred</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">sample_elbo</span><span class="p">(</span>
                <span class="n">X</span><span class="p">,</span>  <span class="c1"># type: ignore[arg-type]</span>
                <span class="n">y</span><span class="p">,</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">,</span>
                <span class="n">n_samples</span><span class="p">,</span>
                <span class="n">n_batches</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">y_pred</span> <span class="o">=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_score</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">valid_running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="n">avg_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">valid_running_loss</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">score</span><span class="p">,</span> <span class="n">avg_loss</span>

    <span class="k">def</span> <span class="nf">_get_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">metric</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;regression&quot;</span><span class="p">:</span>
                <span class="n">score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metric</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
                <span class="n">score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metric</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">y_pred</span><span class="p">),</span> <span class="n">y</span><span class="p">)</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
                <span class="n">score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metric</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">y</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">score</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">None</span>

    <span class="k">def</span> <span class="nf">_predict</span><span class="p">(</span>  <span class="c1"># noqa: C901</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
        <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
        <span class="n">return_samples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>

        <span class="n">test_set</span> <span class="o">=</span> <span class="n">TensorDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_tab</span><span class="p">))</span>
        <span class="n">test_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">dataset</span><span class="o">=</span><span class="n">test_set</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_workers</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="n">test_steps</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span> <span class="o">//</span> <span class="n">test_loader</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>  <span class="c1"># type: ignore[arg-type]</span>

        <span class="n">preds_l</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="k">with</span> <span class="n">trange</span><span class="p">(</span><span class="n">test_steps</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="k">as</span> <span class="n">tt</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">Xl</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">tt</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">):</span>
                    <span class="n">tt</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="s2">&quot;predict&quot;</span><span class="p">)</span>

                    <span class="k">try</span><span class="p">:</span>
                        <span class="n">X</span> <span class="o">=</span> <span class="n">Xl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
                    <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
                        <span class="n">X</span> <span class="o">=</span> <span class="n">Xl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>

                    <span class="k">if</span> <span class="n">return_samples</span><span class="p">:</span>
                        <span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)])</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
                        <span class="n">preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>

                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
                        <span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
                        <span class="n">preds</span> <span class="o">=</span> <span class="p">(</span>
                            <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
                            <span class="k">if</span> <span class="n">return_samples</span>
                            <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                        <span class="p">)</span>

                    <span class="n">preds_cpu</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
                    <span class="n">preds_l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">preds_cpu</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

        <span class="k">return</span> <span class="n">preds_l</span>
</code></pre></div></td></tr></table></div>
              </details>



  <div class="doc doc-children">









<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.training.BayesianTrainer.fit" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">fit</span>


<a href="#pytorch_widedeep.training.BayesianTrainer.fit" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">fit</span><span class="p">(</span>
    <span class="n">X_tab</span><span class="p">,</span>
    <span class="n">target</span><span class="p">,</span>
    <span class="n">X_tab_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">target_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">n_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
    <span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
    <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
    <span class="n">n_train_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
    <span class="n">n_val_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Fit method.</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>X_tab</code>
            </td>
            <td>
                  <code><span title="numpy.ndarray">ndarray</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>tabular dataset</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>target</code>
            </td>
            <td>
                  <code><span title="numpy.ndarray">ndarray</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>target values</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>X_tab_val</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="numpy.ndarray">ndarray</span>]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>validation data</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>target_val</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="numpy.ndarray">ndarray</span>]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>validation target values</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>val_split</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[float]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>An alterative to passing the validation set is to use a train/val
split fraction via <code>val_split</code></p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>n_epochs</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>number of epochs</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>validation_freq</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>epochs validation frequency</p>
              </div>
            </td>
            <td>
                  <code>1</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>batch_size</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>batch size</p>
              </div>
            </td>
            <td>
                  <code>32</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>n_train_samples</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>number of samples to average over during the training process.
See <a href="https://arxiv.org/pdf/1505.05424.pdf">Weight Uncertainty in Neural Networks</a> for details.</p>
              </div>
            </td>
            <td>
                  <code>2</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>n_val_samples</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>number of samples to average over during the validation process.
See <a href="https://arxiv.org/pdf/1505.05424.pdf">Weight Uncertainty in Neural Networks</a> for details.</p>
              </div>
            </td>
            <td>
                  <code>2</code>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/training/bayesian_trainer.py</code></summary>
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    <span class="bp">self</span><span class="p">,</span>
    <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
    <span class="n">target</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
    <span class="n">X_tab_val</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">target_val</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">val_split</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">n_epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
    <span class="n">validation_freq</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
    <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
    <span class="n">n_train_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
    <span class="n">n_val_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
<span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fit method.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    X_tab: np.ndarray,</span>
<span class="sd">        tabular dataset</span>
<span class="sd">    target: np.ndarray</span>
<span class="sd">        target values</span>
<span class="sd">    X_tab_val: np.ndarray, Optional, default = None</span>
<span class="sd">        validation data</span>
<span class="sd">    target_val: np.ndarray, Optional, default = None</span>
<span class="sd">        validation target values</span>
<span class="sd">    val_split: float, Optional. default=None</span>
<span class="sd">        An alterative to passing the validation set is to use a train/val</span>
<span class="sd">        split fraction via `val_split`</span>
<span class="sd">    n_epochs: int, default=1</span>
<span class="sd">        number of epochs</span>
<span class="sd">    validation_freq: int, default=1</span>
<span class="sd">        epochs validation frequency</span>
<span class="sd">    batch_size: int, default=32</span>
<span class="sd">        batch size</span>
<span class="sd">    n_train_samples: int, default=2</span>
<span class="sd">        number of samples to average over during the training process.</span>
<span class="sd">        See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.</span>
<span class="sd">    n_val_samples: int, default=2</span>
<span class="sd">        number of samples to average over during the validation process.</span>
<span class="sd">        See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>

    <span class="n">train_set</span><span class="p">,</span> <span class="n">eval_set</span> <span class="o">=</span> <span class="n">tabular_train_val_split</span><span class="p">(</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">,</span> <span class="n">X_tab</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">X_tab_val</span><span class="p">,</span> <span class="n">target_val</span><span class="p">,</span> <span class="n">val_split</span>
    <span class="p">)</span>
    <span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">dataset</span><span class="o">=</span><span class="n">train_set</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_workers</span>
    <span class="p">)</span>
    <span class="n">train_steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">eval_set</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">eval_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">dataset</span><span class="o">=</span><span class="n">eval_set</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_workers</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="n">eval_steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_loader</span><span class="p">)</span>

    <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_begin</span><span class="p">(</span>
        <span class="p">{</span>
            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="n">batch_size</span><span class="p">,</span>
            <span class="s2">&quot;train_steps&quot;</span><span class="p">:</span> <span class="n">train_steps</span><span class="p">,</span>
            <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span>
        <span class="p">}</span>
    <span class="p">)</span>
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
        <span class="n">epoch_logs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_epoch_begin</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">logs</span><span class="o">=</span><span class="n">epoch_logs</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">train_running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="k">with</span> <span class="n">trange</span><span class="p">(</span><span class="n">train_steps</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="k">as</span> <span class="n">t</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">):</span>
                <span class="n">t</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="s2">&quot;epoch </span><span class="si">%i</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
                <span class="n">train_score</span><span class="p">,</span> <span class="n">train_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_step</span><span class="p">(</span>
                    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_train_samples</span><span class="p">,</span> <span class="n">train_steps</span><span class="p">,</span> <span class="n">batch_idx</span>
                <span class="p">)</span>
                <span class="n">print_loss_and_metric</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">train_score</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_batch_end</span><span class="p">(</span><span class="n">batch</span><span class="o">=</span><span class="n">batch_idx</span><span class="p">)</span>
        <span class="n">epoch_logs</span> <span class="o">=</span> <span class="n">save_epoch_logs</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">train_score</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">)</span>

        <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="n">eval_set</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">epoch</span> <span class="o">%</span> <span class="n">validation_freq</span> <span class="o">==</span> <span class="p">(</span>
            <span class="n">validation_freq</span> <span class="o">-</span> <span class="mi">1</span>
        <span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_eval_begin</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">valid_running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
            <span class="k">with</span> <span class="n">trange</span><span class="p">(</span><span class="n">eval_steps</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="k">as</span> <span class="n">v</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">eval_loader</span><span class="p">):</span>
                    <span class="n">v</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="s2">&quot;valid&quot;</span><span class="p">)</span>
                    <span class="n">val_score</span><span class="p">,</span> <span class="n">val_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_step</span><span class="p">(</span>
                        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_val_samples</span><span class="p">,</span> <span class="n">train_steps</span><span class="p">,</span> <span class="n">i</span>
                    <span class="p">)</span>
                    <span class="n">print_loss_and_metric</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_score</span><span class="p">)</span>
            <span class="n">epoch_logs</span> <span class="o">=</span> <span class="n">save_epoch_logs</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">,</span> <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_score</span><span class="p">,</span> <span class="s2">&quot;val&quot;</span><span class="p">)</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau</span><span class="p">:</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau_criterion</span> <span class="o">==</span> <span class="s2">&quot;loss&quot;</span><span class="p">:</span>
                    <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="n">val_loss</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">on_epoch_end_metric</span> <span class="o">=</span> <span class="n">val_score</span><span class="p">[</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">reducelronplateau_criterion</span>
                    <span class="p">]</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_epoch_end</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">epoch_logs</span><span class="p">,</span> <span class="n">on_epoch_end_metric</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">)</span>
            <span class="k">break</span>

    <span class="bp">self</span><span class="o">.</span><span class="n">callback_container</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="n">epoch_logs</span><span class="p">)</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">_restore_best_weights</span><span class="p">()</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.training.BayesianTrainer.predict" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">predict</span>


<a href="#pytorch_widedeep.training.BayesianTrainer.predict" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">predict</span><span class="p">(</span>
    <span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">return_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">256</span>
<span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Returns the predictions</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>X_tab</code>
            </td>
            <td>
                  <code><span title="numpy.ndarray">ndarray</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>tabular dataset</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>n_samples</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>number of samples that will be either returned or averaged to
produce an overal prediction</p>
              </div>
            </td>
            <td>
                  <code>5</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>return_samples</code>
            </td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating whether the n samples will be averaged or directly returned</p>
              </div>
            </td>
            <td>
                  <code>False</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>batch_size</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>batch size</p>
              </div>
            </td>
            <td>
                  <code>256</code>
            </td>
          </tr>
      </tbody>
    </table>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code>np.ndarray:</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>array with the predictions</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/training/bayesian_trainer.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">258</span>
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<span class="normal">294</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>  <span class="c1"># type: ignore[return]</span>
    <span class="bp">self</span><span class="p">,</span>
    <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
    <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
    <span class="n">return_samples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the predictions</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    X_tab: np.ndarray,</span>
<span class="sd">        tabular dataset</span>
<span class="sd">    n_samples: int, default=5</span>
<span class="sd">        number of samples that will be either returned or averaged to</span>
<span class="sd">        produce an overal prediction</span>
<span class="sd">    return_samples: bool, default = False</span>
<span class="sd">        Boolean indicating whether the n samples will be averaged or directly returned</span>
<span class="sd">    batch_size: int, default = 256</span>
<span class="sd">        batch size</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    np.ndarray:</span>
<span class="sd">        array with the predictions</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">preds_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">return_samples</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
    <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span>
    <span class="n">axis</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="mi">1</span>

    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;regression&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.training.BayesianTrainer.predict_proba" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">predict_proba</span>


<a href="#pytorch_widedeep.training.BayesianTrainer.predict_proba" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">predict_proba</span><span class="p">(</span>
    <span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">return_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">256</span>
<span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Returns the predicted probabilities</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>X_tab</code>
            </td>
            <td>
                  <code><span title="numpy.ndarray">ndarray</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>tabular dataset</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>n_samples</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>number of samples that will be either returned or averaged to
produce an overal prediction</p>
              </div>
            </td>
            <td>
                  <code>5</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>return_samples</code>
            </td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating whether the n samples will be averaged or directly returned</p>
              </div>
            </td>
            <td>
                  <code>False</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>batch_size</code>
            </td>
            <td>
                  <code>int</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>batch size</p>
              </div>
            </td>
            <td>
                  <code>256</code>
            </td>
          </tr>
      </tbody>
    </table>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><span title="numpy.ndarray">ndarray</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>array with the probabilities per class</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/training/bayesian_trainer.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">296</span>
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    <span class="bp">self</span><span class="p">,</span>
    <span class="n">X_tab</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
    <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
    <span class="n">return_samples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the predicted probabilities</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    X_tab: np.ndarray,</span>
<span class="sd">        tabular dataset</span>
<span class="sd">    n_samples: int, default=5</span>
<span class="sd">        number of samples that will be either returned or averaged to</span>
<span class="sd">        produce an overal prediction</span>
<span class="sd">    return_samples: bool, default = False</span>
<span class="sd">        Boolean indicating whether the n samples will be averaged or directly returned</span>
<span class="sd">    batch_size: int, default = 256</span>
<span class="sd">        batch size</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    np.ndarray</span>
<span class="sd">        array with the probabilities per class</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">preds_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict</span><span class="p">(</span><span class="n">X_tab</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">return_samples</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
    <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span> <span class="k">if</span> <span class="n">return_samples</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">preds_l</span><span class="p">)</span>

    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">return_samples</span><span class="p">:</span>
            <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
            <span class="n">probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">])</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_samples</span><span class="p">):</span>
                <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
                <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
            <span class="n">probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">])</span>
            <span class="n">probs</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">preds</span>
            <span class="n">probs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">preds</span>
        <span class="k">return</span> <span class="n">probs</span>
    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">preds</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.training.BayesianTrainer.save" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">save</span>


<a href="#pytorch_widedeep.training.BayesianTrainer.save" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">save</span><span class="p">(</span>
    <span class="n">path</span><span class="p">,</span>
    <span class="n">save_state_dict</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">model_filename</span><span class="o">=</span><span class="s2">&quot;bayesian_model.pt&quot;</span><span class="p">,</span>
<span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Saves the model, training and evaluation history to disk</p>
<p>The <code>Trainer</code> class is built so that it 'just' trains a model. With
that in mind, all the torch related parameters (such as optimizers or
learning rate schedulers) have to be defined externally and then
passed to the <code>Trainer</code>. As a result, the <code>Trainer</code> does not
generate any attribute or additional data products that need to be
saved other than the <code>model</code> object itself, which can be saved as
any other torch model (e.g. <code>torch.save(model, path)</code>).</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>path</code>
            </td>
            <td>
                  <code>str</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>path to the directory where the model and the feature importance
attribute will be saved.</p>
              </div>
            </td>
            <td>
                <em>required</em>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>save_state_dict</code>
            </td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating whether to save directly the model or the
model's state dictionary</p>
              </div>
            </td>
            <td>
                  <code>False</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>model_filename</code>
            </td>
            <td>
                  <code>str</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>filename where the model weights will be store</p>
              </div>
            </td>
            <td>
                  <code>&#39;bayesian_model.pt&#39;</code>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/training/bayesian_trainer.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">341</span>
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<span class="normal">388</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">save</span><span class="p">(</span>
    <span class="bp">self</span><span class="p">,</span>
    <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
    <span class="n">save_state_dict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="n">model_filename</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bayesian_model.pt&quot;</span><span class="p">,</span>
<span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Saves the model, training and evaluation history to disk</span>

<span class="sd">    The `Trainer` class is built so that it &#39;just&#39; trains a model. With</span>
<span class="sd">    that in mind, all the torch related parameters (such as optimizers or</span>
<span class="sd">    learning rate schedulers) have to be defined externally and then</span>
<span class="sd">    passed to the `Trainer`. As a result, the `Trainer` does not</span>
<span class="sd">    generate any attribute or additional data products that need to be</span>
<span class="sd">    saved other than the `model` object itself, which can be saved as</span>
<span class="sd">    any other torch model (e.g. `torch.save(model, path)`).</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    path: str</span>
<span class="sd">        path to the directory where the model and the feature importance</span>
<span class="sd">        attribute will be saved.</span>
<span class="sd">    save_state_dict: bool, default = False</span>
<span class="sd">        Boolean indicating whether to save directly the model or the</span>
<span class="sd">        model&#39;s state dictionary</span>
<span class="sd">    model_filename: str, Optional, default = &quot;wd_model.pt&quot;</span>
<span class="sd">        filename where the model weights will be store</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">save_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
    <span class="n">history_dir</span> <span class="o">=</span> <span class="n">save_dir</span> <span class="o">/</span> <span class="s2">&quot;history&quot;</span>
    <span class="n">history_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="c1"># the trainer is run with the History Callback by default</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">history_dir</span> <span class="o">/</span> <span class="s2">&quot;train_eval_history.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">teh</span><span class="p">:</span>
        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">teh</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span>

    <span class="n">has_lr_history</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span>
        <span class="p">[</span><span class="n">clbk</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;LRHistory&quot;</span> <span class="k">for</span> <span class="n">clbk</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">callbacks</span><span class="p">]</span>
    <span class="p">)</span>
    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">has_lr_history</span><span class="p">:</span>
        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">history_dir</span> <span class="o">/</span> <span class="s2">&quot;lr_history.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">lrh</span><span class="p">:</span>
            <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_history</span><span class="p">,</span> <span class="n">lrh</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span>

    <span class="n">model_path</span> <span class="o">=</span> <span class="n">save_dir</span> <span class="o">/</span> <span class="n">model_filename</span>
    <span class="k">if</span> <span class="n">save_state_dict</span><span class="p">:</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">model_path</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">model_path</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

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